The present invention generally relates to supervision or monitoring of disturbances in physical processes.
As a specific field in this regard performance management according to TMN (Telecommunications Management Networks) standards of telecommunication operations can be mentioned, cf. ITU-T Recommendation M.3400.
In the specific field of telecommunications and telecommunication systems, there are many examples of disturbances, such as parity errors, sporadic hardware faults, bit-correction errors, cyclic-redundancy-check (CRC) errors, congested call attempts, synchronization slips, protocol errors, signaling errors in line or register signaling, program exceptions during run-time, and violations of the software contract at an interface.
There are also many cases of disturbances in processes outside the field of telecommunications, such as errors appearing when making a copy on a photocopier, false results in a blood test, production faults in the manufacture of an electronic component or a printed-circuit board.
All such disturbances are unavoidable, and there is no reason to intervene for a single disturbance in order to find its cause. However, it is necessary to monitor automatically the disturbance rate, normally referred to as the disturbance frequency. If the disturbance frequency remains at a relatively low predictable level, this can be accepted. But if the rate of disturbances rises to an unacceptable level, then the monitoring mechanism must raise an alarm, or send a notification, requesting manual intervention to find the cause of the excess disturbances.
Currently, the expression Quality-Of-Service (QOS) measurement is used, as part of the performance management specified by TMN standards. QOS measurements are well specified by standards, cf. for example, ITU-T G.821 on #7 signaling, concerning error rates.
In monitoring disturbances in physical processes, there are a number of different algorithms that can be used in the QOS measurements. Such a QOS algorithm generally includes a number of so-called threshold parameters, the values of which must be selected before the algorithm can be put into operation. Examples of algorithms used in QOS measurements are the so-called Leaky Bucket algorithm and the Quotient algorithm. The Quotient algorithm as well as the Leaky Bucket algorithm are potentially well usable algorithms for QOS measurements.
For a better understanding of the problems related to setting thresholds for QOS algorithms, the Quotient algorithm will now be briefly described with reference to FIG. 1. The Quotient algorithm, also referred to as the Quota algorithm, makes use of two counters, a first counter for base events and a second counter for disturbances. FIG. 1 is a schematic diagram illustrating the counter values of the counters used by the Quotient algorithm. The first so-called base-event counter increments by one for each base event. The second so-called disturbance counter increments by one for each disturbance. Two thresholds are specified, one for each counter. The threshold for the base-event counter is designated H, and the threshold for the disturbance counter is designated T. The thresholds are set in such a way that the base-event counter normally reaches its threshold first. However, for very high disturbance frequencies, the disturbance counter should reach its threshold first such that an alarm can be given or a notification sent. In other words, if the base-event counter reaches its threshold H first, before the disturbance counter reaches T, the disturbance frequency is assumed to be at an acceptable level. If the disturbance counter reaches its threshold T first, before the base-event counter reaches H, then it is assumed that the disturbance frequency is too high.
The setting of the thresholds for the Quotient algorithm is obviously critical for the overall operation of the algorithm. In the prior art, thresholds for QOS algorithms, such as the Quotient algorithm, have been selected in a more or less arbitrary manner. There have been no satisfactory guidelines on how to set the threshold parameters in a systematic way so as to obtain meaningful results.
In practice, threshold parameters for QOS algorithms are set empirically based on judgment and experience, and often, the results from QOS measurements are so unreliable that they are worse than useless. They give false results, and can be such an irritant to maintenance personnel that the monitoring of the disturbances are turned off.
U.S. Pat. No. 5,377,195 illustrates a specific form of disturbance monitoring in which the Leaky Bucket algorithm is utilized.
U.S. Pat. No. 5,629,927 discloses a system and method for monitoring and controlling ATM networks. Selected ranges of contiguous non-empty cells and of contiguous empty cells are monitored, and corresponding count values are outputted. The count values are analyzed and control signals used for reordering or changing the time of transmission of data are generated. For the count values of non-empty cells and empty cells, a respective threshold is used for distinguishing a desirable operative region from a control region.
The Patent Publication WO 97/12323 relates to in-service performance monitoring of high-speed synchronous digital telecommunications signals. More particularly, WO 97/12323 discloses a method and apparatus for generating and clearing an excessive bit-error-rate alarm by using a time-window technique.
The present invention overcomes these and other drawbacks of the prior art arrangements.
It is a general object of the invention to improve the performance and quality of the monitoring of disturbances in physical processes.
Another object of the invention is to find a rigorous and systematic method for setting threshold values for algorithms that control the monitoring of disturbances in physical processes.
It is also an object of the invention to fully utilize the information of the physical disturbance process that can be obtained from QOS measurements controlled by the Quotient algorithm.
Yet another object of the invention is to provide a system for monitoring disturbances in a physical process using the Quotient algorithm, where the thresholds for the Quotient algorithm are adjusted in a systematic and efficient manner, so as to obtain meaningful and satisfactory results of the QOS measurements.
These and other objects are met by the invention as defined by the accompanying claims.
In accordance with a preferred embodiment of the invention, the Quotient algorithm is used for controlling the monitoring of disturbances in a physical process and disturbance-counting values acquired in the monitoring are utilized to determine a disturbance frequency and a peakedness value for the disturbance process. The peakedness value is used as a measure of the bursty behavior of the disturbances. The disturbance frequency and the peakedness value determined from the acquired disturbance-counting values are utilized in calculating more refined thresholds for the algorithm. The thresholds initially set for the Quotient algorithm are replaced by these more refined thresholds, thus improving the performance and quality of the disturbance monitoring.
In practice, disturbances do not generally occur purely at random but in bursts. It is thus necessary that the bursty behavior of the disturbances in the physical process is treated correctly. According to an essential feature of the invention, a peakedness hypothesis is defined stating that sufficient information about the bursty behavior of the disturbance process is comprised within the value of the peakedness factor together with the disturbance frequency.
The peakedness factor is defined by ITU-T to be the ratio of the variance to the mean of a random variable. The general idea according to the invention is to utilize an inherent feature of the Quotient algorithm itself to find a random variable of interest for determining a value of the peakedness factor for the disturbance process. The peakedness factor for the disturbance process is defined as the ratio of the variance to the mean of occurrences of disturbances in the physical process. According to a preferred embodiment of the invention, a single QOS measurement is called a xe2x80x9cmeasurement periodxe2x80x9d and is defined as the time taken for the base-event counter to reach its threshold. The value of the disturbance counter at the end of the measurement period is a random variable referred to as a disturbance-counting value, and this random variable is used in determining the value of the peakedness factor for the disturbance process.
The mathematical framework which makes it possible to determine the peakedness factor of the disturbance process is primarily based on the assumption that the disturbance-counting values at the end of the predetermined measurement periods are Pi-distributed. The Pi-distribution of the disturbance-counting values has a certain peakedness, which can be calculated by using an averaging algorithm such as the moving-geometric-mean algorithm or the sliding-window algorithm.
In particular, the moving-geometric-mean algorithm is suitable for processing the disturbance-counting values to determine the peakedness as well as the disturbance frequency of the disturbance process.
It should be understood that the disturbance frequency and the peakedness value relate to the disturbances of the physical process and not to the particular algorithm that controls the monitoring of the disturbances. Consequently, the disturbance frequency and the peakedness value determined by using the Quotient algorithm can be used to set the parameters for other monitoring algorithms, such as the Leaky Bucket algorithm.
The invention offers the following advantages:
improved monitoring performance;
an efficient and systematic approach to find thresholds for monitoring algorithms so as to obtain meaningful results; and
efficient utilization of the information obtained from QOS measurements controlled by the Quotient algorithm.
Other advantages offered by the present invention will be appreciated upon reading of the below description of the embodiments of the invention.